Spatial transcriptomics in development and disease

Abstract

The proper functioning of diverse biological systems depends on the spatial organization of their cells, a critical factor for biological processes like shaping intricate tissue functions and precisely determining cell fate. Nonetheless, conventional bulk or single-cell RNA sequencing methods were incapable of simultaneously capturing both gene expression profiles and the spatial locations of cells. Hence, a multitude of spatially resolved technologies have emerged, offering a novel dimension for investigating regional gene expression, spatial domains, and interactions between cells. Spatial transcriptomics (ST) is a method that maps gene expression in tissue while preserving spatial information. It can reveal cellular heterogeneity, spatial organization and functional interactions in complex biological systems. ST can also complement and integrate with other omics methods to provide a more comprehensive and holistic view of biological systems at multiple levels of resolution. Since the advent of ST, new methods offering higher throughput and resolution have become available, holding significant potential to expedite fresh insights into comprehending biological complexity. Consequently, a rapid increase in associated research has occurred, using these technologies to unravel the spatial complexity during developmental processes or disease conditions. In this review, we summarize the recent advancement of ST in historical, technical, and application contexts. We compare different types of ST methods based on their principles and workflows, and present the bioinformatics tools for analyzing and integrating ST data with other modalities. We also highlight the applications of ST in various domains of biomedical research, especially development and diseases. Finally, we discuss the current limitations and challenges in the field, and propose the future directions of ST.

A comprehensive review: synergizing stem cell and embryonic development knowledge in mouse and human integrated stem cell-based embryo models

“…While dissociation-based approaches struggle to preserve tissue structure, thereby restricting expression analysis within the natural context, spatial transcriptomic technologies aim to overcome this limitation. Whereas some studies have employed spatial transcriptomics technologies to study mouse ( Peng et al, 2019 ; Chen et al, 2022 ; Sampath Kumar et al, 2023 ; Srivatsan et al, 2021 ) and human (Xu et al, 2023; Zeng et al, 2023 ) embryogenesis, the employed techniques currently still face too many technical and computational challenges (reviewed by ( Zhang et al, 2023 ; Zhou et al, 2023 )) to be routinely used for comparative analyses.…”

Cellular diversity through space and time: adding new dimensions to GBM therapeutic development

“…Notably, several spatial barcoding techniques, including HDST, Stereo-seq, and Seq-SCOPE, are high resolution and capture spots at subcellular size (0.5-2 microns), thereby foregoing the need for spatial deconvolution. These approaches have yet to be used in GBM studies, likely due to technical challenges and strict instrument requirements (Wang Y. et al, 2023).…”

“…In the broad field of cancer biology, the aforementioned technologies have granted revolutionary insight into the dynamic and heterogeneous nature of cancers. In particular, spatial omics have unveiled spatial diversity of cancer cell states, invasive progression of tumors, spatially distinct cellular transitions in response to stimuli, cellular interactions within the TME, and tissue localization of treatment-resistant cancer cells ( Zhou et al, 2023 ).…”

scBSP: A fast and accurate tool for identifying spatially variable genes from spatial transcriptomic data

“…Spatially Resolved Transcriptomics (SRT) has evolved rapidly in biological and biomedical research (1–4). Early technologies in SRT, such as single-molecule fluorescence in situ hybridization (smFISH) (5) and sequencing-based approaches (e.g., 10X Visium), were primarily designed for small samples on two-dimensional (2D) slices with relatively low spatial resolution.…”